---

name: e2e-testing compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent e2e testing with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: e2e-testing, e2e testing, how do i e2e-testing, orchestrate e2e-testing, automate e2e-testing, agent e2e-testing, selenium, unit tests archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

---

E2E Testing

Orchestrates intelligent skill selection and execution for e2e testing workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def orchestrate_e2e_suite(
    test_manifest: List[Dict],
    environment_state: Dict,
    browser_pool: List[Dict]
) -> List[Dict]:
    """Orchestrate E2E test execution by routing tests to optimal browser/environment combos.
    
    Evaluates test requirements against available browser contexts and environment health.
    Applies flakiness history to schedule unstable tests during low-traffic windows.
    
    Args:
        test_manifest: List of test specs with required browsers, auth types, and endpoints
        environment_state: Current state of staging/prod-like environments
        browser_pool: Available browser contexts with version and capability metadata
        
    Returns:
        Ordered execution plan mapping tests to specific browser contexts
    """
    execution_plan = []
    for test in test_manifest:
        required_browser = test.get("target_browser", "chromium")
        requires_auth = test.get("auth_type") == "oauth2"
        is_flaky = test.get("historical_flakiness", 0) > 0.3
        
        # Filter available browsers by capability and version
        compatible_browsers = [
            b for b in browser_pool 
            if b["engine"] == required_browser and b["version"] >= test.get("min_version", "110")
        ]
        
        if not compatible_browsers:
            raise ValueError(f"No compatible {required_browser} context for test {test['id']}")
            
        # Select browser with lowest current load, prioritizing stable contexts for flaky tests
        selected_context = min(
            compatible_browsers,
            key=lambda b: (b["current_load"], 0 if not is_flaky else 1)
        )
        
        execution_plan.append({
            "test_id": test["id"],
            "browser_context": selected_context["id"],
            "auth_setup": _configure_auth_session(requires_auth, environment_state["auth_endpoint"]),
            "network_mock": _apply_api_mocks(test.get("mock_endpoints", [])),
            "timeout_ms": test.get("timeout", 30000)
        })
        
    return execution_plan

Pattern 2: Execution with Fallback

def execute_test_with_resilience(
    test_plan: Dict,
    test_runner: object,
    diagnostic_store: object
) -> Dict:
    """Execute a single E2E test with built-in resilience and diagnostic fallback.
    
    Implements retry logic for transient network/UI issues, captures screenshots/video on failure,
    and falls back to lightweight smoke assertions if full DOM interaction fails.
    
    Args:
        test_plan: Execution plan generated by orchestrate_e2e_suite
        test_runner: Initialized Playwright/Cypress runner instance
        diagnostic_store: Storage backend for screenshots, traces, and logs
        
    Returns:
        Test result dict with status, duration, and diagnostic artifacts
    """
    max_retries = 2
    last_error = None
    page = None
    
    for attempt in range(max_retries + 1):
        try:
            context = test_runner.new_context(
                base_url=test_plan["base_url"],
                storage_state=test_plan["auth_setup"],
                extra_http_headers=test_plan.get("headers", {})
            )
            page = context.new_page()
            for mock in test_plan["network_mock"]:
                page.route(mock["pattern"], mock["handler"])
                
            result = test_runner.run_steps(page, test_plan["steps"])
            diagnostic_store.save_trace(page, test_plan["test_id"], attempt)
            return {
                "status": "passed",
                "test_id": test_plan["test_id"],
                "duration_ms": result["elapsed"],
                "attempts": attempt + 1,
                "artifacts": diagnostic_store.get_latest(test_plan["test_id"])
            }
            
        except (TimeoutError, NetworkError, AssertionError) as e:
            last_error = e
            if page:
                diagnostic_store.capture_screenshot(page, test_plan["test_id"], attempt)
                diagnostic_store.capture_console_logs(page, test_plan["test_id"])
            if attempt < max_retries:
                time.sleep(2 ** attempt)
            continue
            
    fallback_result = _run_lightweight_smoke_check(test_plan["base_url"], test_plan["critical_paths"])
    return {
        "status": "flaky" if fallback_result["passed"] else "failed",
        "test_id": test_plan["test_id"],
        "error": str(last_error),
        "fallback_status": fallback_result["status"],
        "artifacts": diagnostic_store.get_latest(test_plan["test_id"])
    }

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

---

Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Related Skills

| Skill | Purpose | |